Information content of reflection-coefficient data versus angle and frequency for geoacoustic inversion
Bibliographic record
Abstract
This paper considers the information content of seabed reflection-coefficientdata (as a function of angle and frequency) to resolve fine seabed structure through geoacoustic inversion. During the 2017 Seabed Characterization Experiment conducted on the New England Mud Patch, two types of measurement systems were employed to collect reflection data. One is a bistatic system with bottom-moored hydrophones and an omni-directional towed source, and the other is a towed system that uses the same source and a 15.4 m acoustic array on the same cable (source separation to the closest sensor is ∼ 27 m). Limited by the source–receiver geometry and data quality, the angular coverages of the reflection data for the bistatic and towed systems are ∼25–58° and ∼43–55°, respectively. This study aims to assess the capabilities of these systems to infer sediment structure using simulations over a frequency band of 1–6 kHz. Inversions show that the data simulated for the bistatic system better resolve fine structure of the seabed even with fewer frequency components, while the data for the towed system must include reflections at higher frequencies to get acceptable results. [Work supported by the Office of Naval Research.]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".